Analysis for doubly repeated omics data from crossover design

Sunghoon Choi; Soo Yeon Park; Hoejin Kim; Oran Kwon; Taesung Park

doi:10.1109/BIBM.2016.7822782

Analysis for doubly repeated omics data from crossover design

Sunghoon Choi
, Soo Yeon Park
, Hoejin Kim
, Oran Kwon
, Taesung Park

Department of Nutritional Science & Food Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Some crossover clinical trials produce doubly repeated omics data with two repeated factors. Linear mixed effect models (LMMs) are commonly applied to the data from the crossover design focusing on the analysis of repeatedly observed omics data themselves. Alternatively, the univariate analyses using the single summary measurements such as differences between time points and incremental area under curve (iAUC) are also widely used. In this study, we compare the performance of both methods for real doubly repeated omics data from a crossover study.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1749-1752
Number of pages	4
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822782
State	Published - 17 Jan 2017
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Crossover design
Linear mixed effect model
Repeated measurements

Access to Document

10.1109/BIBM.2016.7822782

Cite this

Choi, S., Park, S. Y., Kim, H., Kwon, O., & Park, T. (2017). Analysis for doubly repeated omics data from crossover design. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1749-1752). Article 7822782 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822782

Choi, Sunghoon ; Park, Soo Yeon ; Kim, Hoejin et al. / Analysis for doubly repeated omics data from crossover design. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1749-1752 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{bad07541eff2432987233363cb26d451,

title = "Analysis for doubly repeated omics data from crossover design",

abstract = "Some crossover clinical trials produce doubly repeated omics data with two repeated factors. Linear mixed effect models (LMMs) are commonly applied to the data from the crossover design focusing on the analysis of repeatedly observed omics data themselves. Alternatively, the univariate analyses using the single summary measurements such as differences between time points and incremental area under curve (iAUC) are also widely used. In this study, we compare the performance of both methods for real doubly repeated omics data from a crossover study.",

keywords = "Crossover design, Linear mixed effect model, Repeated measurements",

author = "Sunghoon Choi and Park, \{Soo Yeon\} and Hoejin Kim and Oran Kwon and Taesung Park",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822782",

language = "English",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1749--1752",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, \{Xiaohua Tony\} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

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Choi, S, Park, SY, Kim, H, Kwon, O & Park, T 2017, Analysis for doubly repeated omics data from crossover design. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822782, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1749-1752, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822782

Analysis for doubly repeated omics data from crossover design. / Choi, Sunghoon; Park, Soo Yeon; Kim, Hoejin et al.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1749-1752 7822782 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Some crossover clinical trials produce doubly repeated omics data with two repeated factors. Linear mixed effect models (LMMs) are commonly applied to the data from the crossover design focusing on the analysis of repeatedly observed omics data themselves. Alternatively, the univariate analyses using the single summary measurements such as differences between time points and incremental area under curve (iAUC) are also widely used. In this study, we compare the performance of both methods for real doubly repeated omics data from a crossover study.

AB - Some crossover clinical trials produce doubly repeated omics data with two repeated factors. Linear mixed effect models (LMMs) are commonly applied to the data from the crossover design focusing on the analysis of repeatedly observed omics data themselves. Alternatively, the univariate analyses using the single summary measurements such as differences between time points and incremental area under curve (iAUC) are also widely used. In this study, we compare the performance of both methods for real doubly repeated omics data from a crossover study.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Choi S, Park SY, Kim H, Kwon O, Park T. Analysis for doubly repeated omics data from crossover design. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1749-1752. 7822782. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822782

Analysis for doubly repeated omics data from crossover design

Abstract

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Bibliographical note

Keywords

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